In [2]:
# This notebook creates a matrix for the electricity demand from the from the European Commission's Joint Research Centre’s EU Energy Atlas.
# Each plane of the matrix is dedicated for one demand sector.
# The values are in MWh
In [1]:
import rasterio
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
In [2]:
from IPython.core.display import display, HTML
display(HTML("<style>.output_scroll { height: 500px !important; }</style>"))

/var/folders/dq/9f7l1n4s0w5dyky898r6tbzh0000gn/T/ipykernel_62436/2030294557.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
  from IPython.core.display import display, HTML
In [3]:
file_path_industry = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/industry_electricity_demand_2019.tif"  
file_path_others = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/othersectors_electricity_demand_2019.tif" 
file_path_transport = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/transport_electricity_demand_2019.tif" 
file_path_transform = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/transformationinputs_electricity_demand_2019.tif" 
#file_path_nonenergy = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/nonenergyuse_electricity_demand_2019.tif"

file_path_total = "/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Demand/total_electricity_demand_2019.tif"
In [5]:
reference_array = np.load('/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Finalised/Arrays/Reference_array.npy')
In [6]:
#Create matrix for each demand type and convert Toe to MWh

import numpy as np

# Read the TIFF file
def read_tiff(file_path):
    with rasterio.open(file_path) as src:
        data = src.read(1)  # Read the first band (assumes single-band TIFF)
        profile = src.profile  # Metadata profile of the TIFF
    return data, profile

# Create a matrix of values from the TIFF file
def create_matrix(file_path):
    # Read the TIFF file
    data, _ = read_tiff(file_path)  
    return data

matrix_industry = create_matrix(file_path_industry)
matrix_others = create_matrix(file_path_others)
matrix_transport = create_matrix(file_path_transport)
matrix_transform = create_matrix(file_path_transform)
#matrix_nonenergy = create_matrix(file_path_nonenergy)

matrix_total = create_matrix(file_path_total)

#convert arrays from toe to MWh
matrix_industry_MWh = matrix_industry *11.63
matrix_others_MWh = matrix_others *11.63
matrix_transport_MWh = matrix_transport *11.63
matrix_transform_MWh = matrix_transform *11.63
#matrix_nonenergy_MWh = matrix_nonenergy *11.63

matrix_total_MWh = matrix_total *11.63


# Assuming reference_array and matrix_industry_MWh are NumPy arrays of the same shape
matrix_industry_MWh_St = reference_array.copy()
matrix_transport_MWh_St = reference_array.copy()
matrix_transform_MWh_St = reference_array.copy()
matrix_others_MWh_St = reference_array.copy()
#matrix_nonenergy_MWh_St = reference_array.copy()
matrix_total_MWh_St = reference_array.copy()


# Add matrix_industry_MWh values where they are greater than 0
matrix_industry_MWh_St[matrix_industry_MWh > 0] += matrix_industry_MWh[matrix_industry_MWh > 0]
matrix_others_MWh_St[matrix_others_MWh > 0] += matrix_others_MWh[matrix_others_MWh > 0]
matrix_transport_MWh_St[matrix_transport_MWh > 0] += matrix_transport_MWh[matrix_transport_MWh > 0]
matrix_transform_MWh_St[matrix_transform_MWh > 0] += matrix_transform_MWh[matrix_transform_MWh > 0]
#matrix_nonenergy_MWh_St[matrix_nonenergy_MWh > 0] += matrix_nonenergy_MWh[matrix_nonenergy_MWh > 0]
matrix_total_MWh_St[matrix_total_MWh > 0] += matrix_total_MWh[matrix_total_MWh > 0]
In [39]:
#Display Matrices

def display_matrix (matrix):
    colors = [(0, 0, 0)] + [(i / 255, i / 255, i / 255) for i in range(256)]
    cmap = LinearSegmentedColormap.from_list("custom_cmap", colors, N=256)

    # Plot the entire matrix
    plt.figure(figsize=(100, 100))
    plt.imshow(matrix, cmap=cmap, vmin=-1, vmax=1)  # Adjust the vmin and vmax if necessary
    plt.colorbar()  # Add a color scale bar
    plt.axis('off')  # Remove axis for clean display
    plt.tight_layout()
    plt.show()
    
display_matrix(matrix_industry_MWh_St)
display_matrix(matrix_others_MWh_St)
display_matrix(matrix_transport_MWh_St)
display_matrix(matrix_transform_MWh_St)
#display_matrix(matrix_nonenergy_MWh)
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In [28]:
def stats_matrix(matrix):
    # Print basic statistics from the matrix
    print("Matrix Statistics:")
    print(f"Maximum value: {np.max(matrix)}")
    print(f"Minimum value: {np.min(matrix)}")
    print(f"Mean value: {np.mean(matrix)}")
    print(f"Standard deviation: {np.std(matrix)}", '\n')
    
stats_matrix(matrix_industry_MWh)
stats_matrix(matrix_industry_MWh_St)
stats_matrix(matrix_others_MWh)
stats_matrix(matrix_others_MWh_St)
stats_matrix(matrix_transport_MWh)
stats_matrix(matrix_transport_MWh_St)
stats_matrix(matrix_transform_MWh)
stats_matrix(matrix_transform_MWh_St)
#stats_matrix(matrix_nonenergy_MWh)

stats_matrix(matrix_total_MWh)
stats_matrix(matrix_total_MWh_St)

Matrix Statistics:
Maximum value: 8771240.0
Minimum value: -11.630000114440918
Mean value: 30.218393325805664
Standard deviation: 3525.478759765625 

Matrix Statistics:
Maximum value: 8771240.0
Minimum value: -1.0
Mean value: 36.84597396850586
Standard deviation: 3525.40283203125 

Matrix Statistics:
Maximum value: 6810602.0
Minimum value: -11.630000114440918
Mean value: 51.92584228515625
Standard deviation: 1759.1580810546875 

Matrix Statistics:
Maximum value: 6810602.0
Minimum value: -1.0
Mean value: 58.55318832397461
Standard deviation: 1758.9141845703125 

Matrix Statistics:
Maximum value: 196030.578125
Minimum value: -11.630000114440918
Mean value: -5.124302864074707
Standard deviation: 94.0363998413086 

Matrix Statistics:
Maximum value: 196030.578125
Minimum value: -1.0
Mean value: 1.5032137632369995
Standard deviation: 93.70645141601562 

Matrix Statistics:
Maximum value: 1011761.5625
Minimum value: -11.630000114440918
Mean value: 2.367494583129883
Standard deviation: 336.0987243652344 

Matrix Statistics:
Maximum value: 1011761.5625
Minimum value: -1.0
Mean value: 8.995014190673828
Standard deviation: 335.8580627441406 

Matrix Statistics:
Maximum value: 8771970.0
Minimum value: -11.630000114440918
Mean value: 101.76504516601562
Standard deviation: 4043.225830078125 

Matrix Statistics:
Maximum value: 8771970.0
Minimum value: -1.0
Mean value: 108.39259338378906
Standard deviation: 4043.0498046875
In [24]:
array = matrix_industry_MWh
y, x = np.unravel_index(np.argmin(array), array.shape)

print(f"Smallest value is at: (y={y}, x={x})")

Smallest value is at: (y=0, x=1701)
In [40]:
#Combine individual matrices into one 3d array (each plane is one type), named matrix_complete
#This axis represents the "stack" of the individual 2D arrays (or "spreadsheets"), with matrix_industry being 0 and matrix_transform being 3

matrix_complete = np.stack((matrix_industry_MWh_St, matrix_others_MWh_St, matrix_transport_MWh_St, matrix_transform_MWh_St), axis=0)
print("Number of planes", len(matrix_complete),'\n',"Number of pixels for y axis:",len(matrix_complete[0]),'\n',"Number of pixels for x axis:",len(matrix_complete[0][0]))

Number of planes 4 
 Number of pixels for y axis: 4478 
 Number of pixels for x axis: 5563
In [26]:
#create matrix of total energy demand form individual sectors
matrix_summed = np.sum(matrix_complete, axis=0)
print("Number of pixels for y axis:",len(matrix_summed),'\n',"Number of pixels for x axis:",len(matrix_summed[0]))

Number of pixels for y axis: 4478 
 Number of pixels for x axis: 5563
In [36]:
#compare calculated total demand to given total demand
comparison_matrix1 = np.stack((matrix_summed, matrix_total_MWh_St), axis=0)
comparison_matrix2 = np.subtract.reduce(comparison_matrix1, axis=0)

def stats_matrix(matrix):
    # Print basic statistics from the matrix
    print("Matrix Statistics:")
    print(f"Maximum value: {np.max(matrix)}")
    print(f"Minimum value: {np.min(matrix)}")
    print(f"Mean value: {np.mean(matrix)}")
    print(f"Standard deviation: {np.std(matrix)}", '\n')
    
stats_matrix(comparison_matrix2)

count = np.sum(comparison_matrix2 > 0)
print("Number of non-matching values:",count)

Matrix Statistics:
Maximum value: 1.0
Minimum value: -3.0
Mean value: -2.4952380657196045
Standard deviation: 1.1222779750823975 

Number of non-matching values: 343051
In [41]:
import numpy as np

# Define the file path
file_path = '/Users/kobabendiks/Desktop/UNI/Bachelor Thesis/Datasets/Finalised Arrays/Demand_array.npy'

# Save an array
np.save(file_path, matrix_complete)
In [ ]: